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Decision support system for age-related macular degeneration using discrete wavelet transform

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Abstract

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, \(k\)-nearest neighbor (\(k\)-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

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Acknowledgments

Authors thank Social Innovation Research Fund (SIRF/Project Code: T1202), Singapore, for providing grant for this research. Also, authors would like to thank National medical research council (NMRC/CSA/045/2012).

Conflict of interest

The authors do not have any related conflict of interest.

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore

    Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Joel E.W. Koh, Chua Kuang Chua, Jen Hong Tan & Choo Min Lim

  2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

  3. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia

    Vinod Chandran

  4. Department of Electronics and TeleCommunication, St Francis Institute of Technology, Mumbai, 400103, India

    Kevin Noronha

  5. National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Novena, 308433, Singapore

    Augustinus Laude

  6. Singapore National Eye Center, Singapore, 168751, Singapore

    Louis Tong

  7. Ocular Surface Research Group, Singapore Eye Research Institute, Singapore, 168751, Singapore

    Louis Tong

  8. Duke-NUS Graduate Medical School, Singapore, 169857, Singapore

    Louis Tong

  9. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore

    Louis Tong

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  1. Muthu Rama Krishnan Mookiah
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  2. U. Rajendra Acharya
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  4. Chua Kuang Chua
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  7. Choo Min Lim
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  9. Augustinus Laude
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  10. Louis Tong
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Correspondence to Muthu Rama Krishnan Mookiah.

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Mookiah, M.R.K., Acharya, U.R., Koh, J.E. et al. Decision support system for age-related macular degeneration using discrete wavelet transform. Med Biol Eng Comput 52, 781–796 (2014). https://doi.org/10.1007/s11517-014-1180-8

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  • DOI: https://doi.org/10.1007/s11517-014-1180-8

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